Photonic Systems Integration

Laboratory

Platform Motion Blur Image Restoration System

Compressive imagers acquire images, or other optical scene information, by a
series of spatially ltered intensity measurements, where the total number of
measurements required depends on the desired image quality. Compressive imaging
(CI) oers a versatile approach to optical sensing which can improve SWaP for
hyperspectral imaging or feature-based optical sensing. Here we report the rst (to
our knowledge) systematic performance comparison of a CI system to a conventional
focal plane imager for binary, grayscale, and natural light (visible color and infrared)
scenes. We generate 1024 1024 images from a range of measurements (0.1%
to 100%) made using digital (Hadamard), grayscale (Discrete Cosine Transform)
and random (noiselet) CI basis sets, and for varying numbers of measurements.
Comparing the outcome of the compressive images to conventionally acquired
images, each made using 1% of full sampling, we conclude that the Hadamard
Transform oered the best performance and yielding images with comparable
aesthetic quality and slightly higher spatial resolution than conventionally acquired
images.